Synaptic and resistive switching behaviors of Sm-doped HfO2 films for bio-inspired neuromorphic calculations

Artificial neural network-based computing is anticipated to surpass the von Neumann bottleneck of traditional computers, thus dramatically boosting computational efficiency and showing a wide range of promising applications. In this paper, sol-gel deposition was used to prepare thin films of samarium-doped hafnium dioxide (Sm:HfO2). When Sm is doped at a concentration of 4%, it mimics biological synapses; meantime, by voltage scanning, an obvious mimicry of resistive switching can be detected, demonstrating that the technology may be applied to simulate biological synapse characteristics, including long-term potentiation (depression), short-term potentiation (depression), paired-pulse facilitation, and learning rules of spike-time-dependent plasticity. Additionally, a pulsed neural network is built on the MNIST dataset to test the memristor's capacity to handle visual input. The findings show the possibility of synthetic synapses in artificial intelligence systems that integrate neuromorphic computing with synaptic brain activity.